Chapter 8 HMSC analysis

8.1 Load data

load("data/data.Rdata")
load("hmsc/fit_model1_250_10.Rdata")

8.2 Variance partitioning

# Compute variance partitioning
varpart=computeVariancePartitioning(m)

varpart$vals %>%
   as.data.frame() %>%
   rownames_to_column(var="variable") %>%
   pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
   mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location")))) %>%
   group_by(variable) %>%
   summarise(mean=mean(value)*100,sd=sd(value)*100) %>%
   tt()

variable	mean	sd
Random: location	37.900015	25.317903
logseqdepth	56.110626	25.796874
sex	4.937460	5.612719
origin	1.051899	1.282563

# Basal tree
varpart_tree <- genome_tree

#Varpart table
varpart_table <- varpart$vals %>%
   as.data.frame() %>%
   rownames_to_column(var="variable") %>%
   pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
   mutate(genome=factor(genome, levels=rev(varpart_tree$tip.label))) %>%
   mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location"))))

#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__"))%>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% varpart_tree$tip.label) %>%
    arrange(match(genome, varpart_tree$tip.label)) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome") %>%
    select(phylum)

colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__"))%>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% varpart_tree$tip.label) %>%
    arrange(match(genome, varpart_tree$tip.label)) %>%
     select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    select(colors) %>%
    pull()

# Basal ggtree
varpart_tree <- varpart_tree %>%
        force.ultrametric(.,method="extend") %>%
        ggtree(., size = 0.3)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

# Add phylum colors next to the tree tips
varpart_tree <- gheatmap(varpart_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
   scale_fill_manual(values=colors_alphabetic)+
      labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
varpart_tree <- varpart_tree + new_scale_fill()

# Add variance stacked barplot
vertical_tree <-  varpart_tree +
       scale_fill_manual(values=c("#506a96","#cccccc","#be3e2b","#f6de6c"))+
        geom_fruit(
             data=varpart_table,
             geom=geom_bar,
             mapping = aes(x=value, y=genome, fill=variable, group=variable),
             pwidth = 2,
             offset = 0.05,
             width= 1,
             orientation="y",
             stat="identity")+
      labs(fill="Variable")

vertical_tree

8.3 Posterior estimates

# Select desired support threshold
support=0.9
negsupport=1-support

# Basal tree
postestimates_tree <- genome_tree

# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
    mutate(value = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
    pivot_wider(names_from = variable, values_from = value) %>%
    #select(genome,sp_vulgaris,area_semi,area_urban,sp_vulgarisxarea_semi,sp_vulgarisxarea_urban,season_spring,season_winter,sp_vulgarisxseason_spring,sp_vulgarisxseason_winter) %>%
    column_to_rownames(var="genome")

#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__")) %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome") %>%
    select(phylum)


colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__")) %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
     select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    select(colors) %>%
    pull()

# Basal ggtree
postestimates_tree <- postestimates_tree %>%
        force.ultrametric(.,method="extend") %>%
        ggtree(., size = 0.3)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

#Add phylum colors next to the tree tips
postestimates_tree <- gheatmap(postestimates_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
      scale_fill_manual(values=colors_alphabetic)+
      labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()

# Add posterior significant heatmap

postestimates_tree <- gheatmap(postestimates_tree, post_beta, offset=0, width=0.5, colnames=TRUE, colnames_position="top",colnames_angle=90, colnames_offset_y=1, hjust=0) +
        scale_fill_manual(values=c("#be3e2b","#f4f4f4","#b2b530"))+
        labs(fill="Trend")

postestimates_tree +
        vexpand(.25, 1) # expand top 

8.4 Correlations

#Compute the residual correlation matrix
OmegaCor = computeAssociations(m)

# Refernece tree (for sorting genomes)
genome_tree_subset <- genome_tree %>%
        keep.tip(., tip=m$spNames) 


#Co-occurrence matrix at the animal level
supportLevel = 0.95
toPlot = ((OmegaCor[[1]]$support>supportLevel)
          + (OmegaCor[[1]]$support<(1-supportLevel))>0)*OmegaCor[[1]]$mean

matrix <- toPlot %>% 
      as.data.frame() %>%
      rownames_to_column(var="genome1") %>%
      pivot_longer(!genome1, names_to = "genome2", values_to = "cor") %>%
      mutate(genome1= factor(genome1, levels=genome_tree_subset$tip.label)) %>%
      mutate(genome2= factor(genome2, levels=genome_tree_subset$tip.label)) %>%
      ggplot(aes(x = genome1, y = genome2, fill = cor)) +
            geom_tile() + 
            scale_fill_gradient2(low = "#be3e2b",
                       mid = "#f4f4f4",
                       high = "#b2b530")+
            theme_void()

htree <- genome_tree_subset %>%
  force.ultrametric(.,method="extend") %>%
  ggtree(.)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

vtree <- genome_tree_subset %>%
  force.ultrametric(.,method="extend") %>%
  ggtree(.)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

#create composite figure
grid.arrange(grobs = list(matrix,vtree),
             layout_matrix = rbind(c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1)))

8.5 Predict responses

# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")

gradient = c("domestic","feral")
gradientlength = length(gradient)

#Treatment-specific gradient predictions
pred <- constructGradient(m, 
                      focalVariable = "origin", 
                      non.focalVariables = list(logseqdepth=list(1),location=list(1))) %>%
            predict(m, Gradient = ., expected = TRUE) %>%
            do.call(rbind,.) %>%
            as.data.frame() %>%
            mutate(origin=rep(gradient,1000)) %>%
            pivot_longer(!origin,names_to = "genome", values_to = "value")

# weights:  9 (4 variable)
initial  value 101.072331 
final  value 91.392443 
converged

8.5.0.1 Element level

elements_table <- genome_gifts %>%
    to.elements(., GIFT_db) %>%
    as.data.frame()

community_elements <- pred %>%
  group_by(origin, genome) %>%
  mutate(row_id = row_number()) %>%
  pivot_wider(names_from = genome, values_from = value) %>%
  ungroup() %>%
  group_split(row_id) %>%
  as.list() %>%
  lapply(., FUN = function(x){x %>%
    select(-row_id) %>%
    column_to_rownames(var = "origin") %>%
    as.data.frame() %>%
    exp() %>%
    t() %>%
    tss() %>%
    to.community(elements_table,.,GIFT_db) %>% 
    as.data.frame() %>%
    rownames_to_column(var="origin")
   })

calculate_slope <- function(x) {
  lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
  coef(lm_fit)[2]
}

element_predictions <- map_dfc(community_elements, function(mat) {
      mat %>%
        column_to_rownames(var = "origin") %>%
        t() %>%
        as.data.frame() %>%
        rowwise() %>%
        mutate(slope = calculate_slope(c_across(everything()))) %>%
        select(slope) }) %>%
      t() %>%
      as.data.frame() %>%
      set_names(colnames(community_elements[[1]])[-1]) %>%
      rownames_to_column(var="iteration") %>%
      pivot_longer(!iteration, names_to="trait",values_to="value") %>%
      group_by(trait) %>%
      summarise(mean=mean(value),
        p1 = quantile(value, probs = 0.1),
        p9 = quantile(value, probs = 0.9),
        positive_support = sum(value > 0)/1000,
        negative_support = sum(value < 0)/1000) %>%
      arrange(-positive_support)

# Positively associated
element_predictions %>%
  filter(mean >0) %>%
  arrange(-positive_support) %>%
  filter(positive_support>=0.9) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
D0205	0.012603387	2.593807e-03	0.022939556	0.954	0.046
D0208	0.010227729	1.642595e-03	0.018507180	0.945	0.055
D0906	0.003565801	1.749126e-04	0.007907557	0.928	0.072
D0504	0.004396738	2.650007e-04	0.009408255	0.921	0.079
B0103	0.009096237	8.776903e-04	0.017087166	0.916	0.084
D0507	0.003697711	6.655942e-05	0.007228665	0.903	0.097

element_predictions %>%
  filter(mean <0) %>%
  arrange(-negative_support) %>%
  filter(negative_support>=0.9) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
D0801	-0.001672597	-0.002483633	-9.833235e-05	0.014	0.986
D0802	-0.001672597	-0.002483633	-9.833235e-05	0.014	0.986
B0310	-0.013442775	-0.024190545	-3.605897e-03	0.025	0.975
D0517	-0.005167475	-0.008180822	-1.341754e-03	0.025	0.975
B0302	-0.005193974	-0.010525780	-6.418008e-04	0.032	0.968
B0709	-0.002110743	-0.003632081	-5.607182e-04	0.034	0.966
D0601	-0.009391013	-0.017719312	-1.923462e-03	0.034	0.966
D0611	-0.004341066	-0.009391699	-2.372652e-04	0.038	0.962
D0903	-0.004341066	-0.009391699	-2.372652e-04	0.038	0.962
B0219	-0.004355929	-0.009480552	-2.632315e-04	0.039	0.961
B0804	-0.017049337	-0.030998939	-3.672770e-03	0.044	0.956
D0807	-0.004530684	-0.008778695	-6.943753e-04	0.044	0.956
D0603	-0.002011208	-0.004002246	-2.931153e-04	0.047	0.953
D0817	-0.005309855	-0.010758020	-5.734667e-04	0.051	0.949
B0303	-0.012060364	-0.022094010	-2.735578e-03	0.052	0.948
D0610	-0.003193577	-0.005075296	-8.978410e-04	0.054	0.946
D0606	-0.006029744	-0.011505444	-8.948160e-04	0.061	0.939
B0603	-0.016268750	-0.032036510	-2.680562e-03	0.063	0.937
D0908	-0.016042713	-0.027618565	-4.081974e-03	0.063	0.937
B0214	-0.021066765	-0.037854042	-4.079732e-03	0.076	0.924
B0401	-0.011800076	-0.023901689	-9.968034e-04	0.078	0.922
D0816	-0.006097811	-0.012262606	-5.143077e-04	0.079	0.921
B0601	-0.009436323	-0.018855245	-6.283849e-04	0.083	0.917
D0508	-0.003593430	-0.007732534	-2.235608e-04	0.084	0.916
D0612	-0.002002036	-0.003174204	-7.712861e-05	0.088	0.912
B0204	-0.016246634	-0.032996834	-1.386663e-03	0.090	0.910
B0708	-0.025619028	-0.048674986	-2.910586e-04	0.097	0.903
B0309	-0.007653490	-0.015286326	-2.801407e-05	0.099	0.901

positive <- element_predictions %>%
  filter(mean >0) %>%
  arrange(mean) %>%
  filter(positive_support>=0.9) %>%
  select(-negative_support) %>%
  rename(support=positive_support)

negative <- element_predictions %>%
  filter(mean <0) %>%
  arrange(mean) %>%
  filter(negative_support>=0.9) %>%
  select(-positive_support) %>%
  rename(support=negative_support)

bind_rows(positive,negative) %>%
  left_join(GIFT_db,by=join_by(trait==Code_element)) %>%
  mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
  ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
      geom_point() +
      geom_errorbar() +
      xlim(c(-0.04,0.04)) +
      geom_vline(xintercept=0) +
      scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
      theme_minimal() +
      labs(x="Regression coefficient",y="Functional trait")

8.5.0.2 Function level

functions_table <- elements_table %>%
    to.functions(., GIFT_db) %>%
    as.data.frame()

community_functions <- pred %>%
  group_by(origin, genome) %>%
  mutate(row_id = row_number()) %>%
  pivot_wider(names_from = genome, values_from = value) %>%
  ungroup() %>%
  group_split(row_id) %>%
  as.list() %>%
  lapply(., FUN = function(x){x %>%
    select(-row_id) %>%
    column_to_rownames(var = "origin") %>%
    as.data.frame() %>%
    exp() %>%
    t() %>%
    tss() %>%
    to.community(functions_table,.,GIFT_db) %>% 
    as.data.frame() %>%
    rownames_to_column(var="origin")
   })

#max-min option
calculate_slope <- function(x) {
  lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
  coef(lm_fit)[2]
}

function_predictions <- map_dfc(community_functions, function(mat) {
      mat %>%
        column_to_rownames(var = "origin") %>%
        t() %>%
        as.data.frame() %>%
        rowwise() %>%
        mutate(slope = calculate_slope(c_across(everything()))) %>%
        select(slope) }) %>%
      t() %>%
      as.data.frame() %>%
      set_names(colnames(community_functions[[1]])[-1]) %>%
      rownames_to_column(var="iteration") %>%
      pivot_longer(!iteration, names_to="trait",values_to="value") %>%
      group_by(trait) %>%
      summarise(mean=mean(value),
        p1 = quantile(value, probs = 0.1),
        p9 = quantile(value, probs = 0.9),
        positive_support = sum(value > 0)/1000,
        negative_support = sum(value < 0)/1000) %>%
      arrange(-positive_support)

# Positively associated
function_predictions %>%
  filter(mean >0) %>%
  arrange(-positive_support) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
D02	0.0084245611	-0.002708007	0.020025834	0.836	0.164
B08	0.0074961511	-0.003350089	0.018284003	0.791	0.209
B01	0.0011568863	-0.005731988	0.008379737	0.625	0.375
S01	0.0007429401	-0.011690216	0.013752273	0.570	0.430

# Negatively associated
function_predictions %>%
  filter(mean <0) %>%
  arrange(-negative_support) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
B03	-1.112411e-02	-0.0187246652	-0.0039909543	0.032	0.968
D08	-1.214619e-03	-0.0023722116	-0.0002337339	0.034	0.966
D06	-3.261848e-03	-0.0071802725	0.0001432296	0.108	0.892
B04	-8.359836e-03	-0.0185309424	0.0011794391	0.135	0.865
D07	-1.206714e-02	-0.0304543194	0.0040195356	0.182	0.818
B06	-6.715638e-03	-0.0162987317	0.0033156299	0.188	0.812
D05	-2.065349e-03	-0.0078384328	0.0033380053	0.209	0.791
D03	-4.331512e-03	-0.0134588083	0.0035668705	0.224	0.776
S03	-1.020199e-02	-0.0337194527	0.0150239871	0.247	0.753
B02	-4.000958e-03	-0.0128546917	0.0041689091	0.263	0.737
D09	-2.283353e-03	-0.0088140914	0.0048311247	0.312	0.688
S02	-4.751123e-03	-0.0151394921	0.0031706467	0.325	0.675
B07	-4.054452e-03	-0.0158206337	0.0080154534	0.331	0.669
B09	-2.002830e-05	-0.0005311183	0.0004142376	0.361	0.639
D01	-4.373472e-04	-0.0049969959	0.0041040520	0.410	0.590
B10	-1.442446e-05	-0.0003132959	0.0002538415	0.473	0.527

positive <- function_predictions %>%
  filter(mean >0) %>%
  arrange(mean) %>%
  filter(positive_support>=0.9) %>%
  select(-negative_support) %>%
  rename(support=positive_support)

negative <- function_predictions %>%
  filter(mean <0) %>%
  arrange(mean) %>%
  filter(negative_support>=0.9) %>%
  select(-positive_support) %>%
  rename(support=negative_support)

bind_rows(positive,negative) %>%
  left_join(GIFT_db,by=join_by(trait==Code_function)) %>%
  mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
  ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
      geom_point() +
      geom_errorbar() +
      xlim(c(-0.02,0.02)) +
      geom_vline(xintercept=0) +
      scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
      theme_minimal() +
      labs(x="Regression coefficient",y="Functional trait")